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1. Standard Error of Estimate:

The standard error of estimation is an estimated standard deviation of the error term u. It also known as standard error of the regression. Standard Error of Estimate is shows variation of observations. It is applied to inspect the accuracy of estimation made. Standard error of estimate tells the accuracy of the estimated figures.

Formula of the standard error of estimate: sqrt(SSE/(n-k))

Following is the calculation of Standard Error of Estimate:

SSE

25843.41

k

2

n

400

N-k

398

SSE/(n-k)

64.9331909548

sqrt(SSE/(n-k))

8.058113362

If standard error is small, the data will be more representative of the true mean. In cases where the standard error is large, the data may have some notable irregularities.

2. Coefficient of Determination:

The coefficient of determination is a statistical analysis that determine explanation of the model and estimated future outcomes. It shows the level of related variability in the data set. The coefficient of determination refers to R-squared and applied to determine correctness of the model. Coefficient of determination tells that variables in given model is certain percentage of observed variation. It is represented as a value between 0 and 1. Closer the value is to 1, the better the fit, or relationship, between the two factors. Thus, in case the R Square is equal to 0.2672, then approximately less than half of the observed variation can be explained by the model.

Formula of Coefficient of determination: MSS/TSS = (TSS − RSS)/TSS